code: def train_one_batch(self, x, y):
      out = self.forward(x)
      y = self.reshape2(y, (-1, 1))
      loss = self.softmax_cross_entropy(out, y)
      self.optimizer(loss)
      return out, loss
  
imports: from __future__ import division
  from __future__ import print_function
  from builtins import range
  import numpy
  import sys
  import argparse
  from tqdm import tqdm
  from singa import device
  from singa import tensor
  from singa import autograd
  from singa import layer
  from singa import model
  from singa import opt
label_desc: Use image node_id 5 for calling the CharRNN obj's underlying member method code with example usage: obj.train_one_batch(x, y) and returns: out,  loss